Home / Business Analytics and Modeling

Telco Customer Churn

Telco Customer Churn

In this article, we analyze and predict customer churn for Telco Customer Churn data.

Dataset

Columns Description
customerID Customer ID
gender Whether the customer is a male or a female
SeniorCitizen Whether the customer is a senior citizen or not (1, 0)
Partner Whether the customer has a partner or not (Yes, No)
Dependents Whether the customer has dependents or not (Yes, No)
tenure Number of months the customer has stayed with the company
PhoneService Whether the customer has a phone service or not (Yes, No)
MultipleLines Whether the customer has multiple lines or not (Yes, No, No phone service)
InternetService Customer’s internet service provider (DSL, Fiber optic, No)
OnlineSecurity Whether the customer has online security or not (Yes, No, No internet service)
OnlineBackup Whether the customer has an online backup or not (Yes, No, No internet service)
DeviceProtection Whether the customer has device protection or not (Yes, No, No internet service)
TechSupport Whether the customer has tech support or not (Yes, No, No internet service)
StreamingTV Whether the customer has streaming TV or not (Yes, No, No internet service)
StreamingMovies Whether the customer has streaming movies or not (Yes, No, No internet service)
Contract The contract term of the customer (Month-to-month, One year, Two years)
PaperlessBilling Whether the customer has paperless billing or not (Yes, No)
PaymentMethod The customer’s payment method (Electronic check, Mailed check, Bank transfer (automatic), Credit card (automatic))
MonthlyCharges The amount charged to the customer monthly
TotalCharges The total amount charged to the customer
Churn Whether the customer churned or not (Yes or No)

Dataset

Train and Test Sets

Similarly, here we use StratifiedKFold which is a variation of k-fold which returns stratified folds: each set contains approximately the same percentage of samples of each target class as the complete set.

Modeling: PyTorch Multi-layer Perceptron (MLP) for Binary Classification

A multi-layer perceptron (MLP) is a class of feedforward artificial neural network (ANN). The algorithm at each iteration uses the Cross-Entropy Loss to measure the loss, and then the gradient and the model update is calculated. At the end of this iterative process, we would reach a better level of agreement between test and predicted sets since the error would be lower from that of the first step.

Setting up Tensor Arrays

Modeling

Model Performance

Confusion Matrix

The confusion matrix allows for visualization of the performance of an algorithm. Note that due to the size of data, here we don't provide a Cross-validation evaluation. In general, this type of evaluation is preferred.

Some of the metrics that we use here to mesure the accuracy: \begin{align} \text{Confusion Matrix} = \begin{bmatrix}T_p & F_p\\ F_n & T_n\end{bmatrix}. \end{align}

where $T_p$, $T_n$, $F_p$, and $F_n$ represent true positive, true negative, false positive, and false negative, respectively.

\begin{align} \text{Precision} &= \frac{T_{p}}{T_{p} + F_{p}},\\ \text{Recall} &= \frac{T_{p}}{T_{p} + F_{n}},\\ \text{F1} &= \frac{2 \times \text{Precision} \times \text{Recall}}{\text{Precision} + \text{Recall}}\\ \text{Balanced-Accuracy (bACC)} &= \frac{1}{2}\left( \frac{T_{p}}{T_{p} + F_{n}} + \frac{T_{n}}{T_{n} + F_{p}}\right ) \end{align}

The accuracy can be a misleading metric for imbalanced data sets. In these cases, a balanced accuracy (bACC) [6] is recommended that normalizes true positive and true negative predictions by the number of positive and negative samples, respectively, and divides their sum by two.

Predictions

Now for any given dataset, we can predict churn

Conclutions

Although the model is doing pretty well considering the complexity of this problem, we can improve the results by designing an iterative optimization that utilizes the accuracy and recall scores.